/**
 * This program is free software, you can redistribute it and/or modify it.
 * Copyright (c) 2025 Huawei Technologies Co., Ltd.
 * This file is a part of the CANN Open Software.
 * Licensed under CANN Open Software License Agreement Version 2.0 (the "License").
 * Please refer to the License for details. You may not use this file except in compliance with the License.
 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING
 * BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
 * See LICENSE in the root of the software repository for the full text of the License.
 */

#include <climits>
#include "aclnn_argmin.h"
#include "argmin.h"
#include "aclnn_kernels/cast.h"
#include "aclnn_kernels/contiguous.h"
#include "aclnn_kernels/reshape.h"
#include "aclnn_kernels/transdata.h"
#include "aclnn_kernels/common/op_error_check.h"
#include "opdev/common_types.h"
#include "opdev/shape_utils.h"
#include "opdev/data_type_utils.h"
#include "opdev/format_utils.h"
#include "opdev/op_dfx.h"
#include "opdev/op_executor.h"
#include "opdev/op_log.h"
#include "opdev/tensor_view_utils.h"
#include "opdev/platform.h"
#include "conversion/fill/op_host/op_api/fill.h"
#include "common/op_api_def.h"
#include "common/aclnn_check.h"

using namespace op;
#ifdef __cplusplus
extern "C" {
#endif

static const std::initializer_list<op::DataType> OUT_DTYPE_SUPPORT_LIST = {
    op::DataType::DT_INT32, op::DataType::DT_INT64};

static const int64_t FIVE_DIM = 5;

static const inline std::initializer_list<DataType>& GetSupportDtypeList(SocVersion socVersion)
{
    static const std::initializer_list<DataType> emptyDtypes = {};
    static const std::map<SocVersion, std::initializer_list<DataType>> dataTypeSupportedMap = {
        {SocVersion::ASCEND310P,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8}},
        {SocVersion::ASCEND910,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8}},
        {SocVersion::ASCEND910B,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8, DataType::DT_BF16}},
        {SocVersion::ASCEND910_93,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8, DataType::DT_BF16}},
        {SocVersion::ASCEND910_95,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8, DataType::DT_BF16}},
        {SocVersion::ASCEND310B,
         {DataType::DT_FLOAT, DataType::DT_FLOAT16, DataType::DT_DOUBLE, DataType::DT_INT8, DataType::DT_INT16,
          DataType::DT_INT32, DataType::DT_INT64, DataType::DT_UINT8, DataType::DT_BF16}},
    };

    auto found = dataTypeSupportedMap.find(socVersion);
    if (found == dataTypeSupportedMap.end()) {
        return emptyDtypes;
    }

    return found->second;
}

static bool CheckDtypeValid(const aclTensor* self, const aclTensor* out)
{
    auto socVersion = GetCurrentPlatformInfo().GetSocVersion();
    const auto& dTypeSupportList = GetSupportDtypeList(socVersion);
    OP_CHECK_DTYPE_NOT_SUPPORT(self, dTypeSupportList, return false);
    OP_CHECK_DTYPE_NOT_SUPPORT(out, OUT_DTYPE_SUPPORT_LIST, return false);

    return true;
}

static bool CheckNotNull(const aclTensor* self, const aclTensor* out)
{
    OP_CHECK_NULL(self, return false);
    OP_CHECK_NULL(out, return false);

    return true;
}

static bool CheckFormat(const aclTensor* self, const aclTensor* out)
{
    if (op::IsPrivateFormat(self->GetViewFormat()) || op::IsPrivateFormat(out->GetViewFormat())) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID,
            "Format only support ND,NCL,NCHW,NHWC,HWCN,NDHWC,NCDHW,"
            "input format is [%s] and out format is [%s].",
            ToString(self->GetViewFormat()).GetString(), ToString(out->GetViewFormat()).GetString());
        return false;
    }
    return true;
}

static bool CheckDimValid(const aclTensor* self, const int64_t dim)
{
    const int64_t dimNum = self->GetViewShape().GetDimNum();
    int64_t minimum = dimNum * (-1);
    int64_t maximum = dimNum - 1;
    if (dimNum == 0) {
        minimum = -1;
        maximum = 0;
    }
    if (dim < minimum || dim > maximum) {
        OP_LOGE(
            ACLNN_ERR_PARAM_INVALID, "dim must be within the range of [%ld, %ld], but it is %ld.", minimum, maximum,
            dim);
        return false;
    }
    return true;
}

static bool CheckSelfDimSizeNonZreo(const aclTensor* self, const int64_t dim)
{
    int64_t dimNum = self->GetViewShape().GetDimNum();
    if (dimNum == 0) {
        return true;
    }

    int64_t realDim = dim;
    if (realDim < 0) {
        realDim = realDim + dimNum;
    }

    if (self->GetViewShape().GetDim(realDim) == 0) {
        OP_LOGE(ACLNN_ERR_PARAM_INVALID, "Excepted reduction dim %ld to have non-zero size.", dim);
        return false;
    }
    return true;
}

static bool CheckShape(const aclTensor* self, const aclTensor* out)
{
    // 所有输入的维度都不能超过8
    OP_CHECK_MAX_DIM(self, MAX_SUPPORT_DIMS_NUMS, return false);
    OP_CHECK_MAX_DIM(out, MAX_SUPPORT_DIMS_NUMS, return false);
    return true;
}

static aclnnStatus CheckParams(const aclTensor* self, const int64_t dim, const aclTensor* out)
{
    // 1. 检查参数是否为空指针
    CHECK_RET(CheckNotNull(self, out), ACLNN_ERR_PARAM_NULLPTR);

    // 2. 检查dim 是否合法
    CHECK_RET(CheckDimValid(self, dim), ACLNN_ERR_PARAM_INVALID);

    // 3. 检查输入的数据类型是否在API支持的数据类型范围之内，需要根据api定义校验
    CHECK_RET(CheckDtypeValid(self, out), ACLNN_ERR_PARAM_INVALID);

    // 4. 检查数据格式是否支持
    CHECK_RET(CheckFormat(self, out), ACLNN_ERR_PARAM_INVALID);

    // 5. 检查shape是否支持
    CHECK_RET(CheckShape(self, out), ACLNN_ERR_PARAM_INVALID);

    // 检查self dim轴的size不为0
    CHECK_RET(CheckSelfDimSizeNonZreo(self, dim), ACLNN_ERR_PARAM_INVALID);

    return ACLNN_SUCCESS;
}

static bool CheckNeedReshape(const aclTensor* self)
{
    auto selfViewShape = self->GetViewShape();
    // when self.dim > 8 and self.dtype is double, it will run tf kernel
    return selfViewShape.GetDimNum() == MAX_SUPPORT_DIMS_NUMS && self->GetDataType() == op::DataType::DT_DOUBLE;
}

static int64_t CalculateShape(
    const aclTensor* self, FVector<int64_t>& newShapeVector, FVector<int64_t>& outShapeVector, const int64_t dim,
    const bool keepdim)
{
    auto selfShape = self->GetViewShape();
    int64_t realDim = dim;
    if (realDim < 0) {
        realDim = selfShape.GetDimNum() + dim;
    }

    //  Calculate out shape
    for (size_t i = 0; i < MAX_SUPPORT_DIMS_NUMS; i++) {
        if (i != (size_t)realDim) {
            outShapeVector.push_back(selfShape[i]);
        } else if (keepdim) {
            outShapeVector.push_back(1);
        }
    }

    // if realDim is less than 5, merge the last two axes, otherwise merge axes 1 and 2
    if (realDim <= FIVE_DIM) {
        for (int64_t i = 0; i <= FIVE_DIM; i++) {
            newShapeVector.push_back(selfShape[i]);
        }
        newShapeVector.push_back(-1);
    } else {
        newShapeVector.push_back(selfShape[0] * selfShape[1]);
        realDim = realDim - 1;
        for (size_t i = 1; i < MAX_SUPPORT_DIMS_NUMS - 1; i++) {
            newShapeVector.push_back(selfShape[i + 1]);
        }
    }
    return realDim;
}

static aclnnStatus HandleScalar(aclTensor* out, aclOpExecutor* executor)
{
    // fill value tensor
    float fillVaule = 0;
    aclScalar* value = executor->AllocScalar(fillVaule);
    const aclTensor* valueTensor = executor->ConvertToTensor(value, out->GetDataType());
    // fill dims tensor
    FVector<int64_t> dimTmp;
    dimTmp.push_back(1);
    aclIntArray* shapeArray = executor->AllocIntArray(dimTmp.data(), dimTmp.size());
    const aclTensor* dims = executor->ConvertToTensor(dimTmp.data(), dimTmp.size(), out->GetDataType());

    auto fillOut = l0op::Fill(dims, valueTensor, shapeArray, executor);
    CHECK_RET(fillOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

    auto viewCopyResult = l0op::ViewCopy(fillOut, out, executor);
    CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

    return ACLNN_SUCCESS;
}

aclnnStatus aclnnArgMinGetWorkspaceSize(
    const aclTensor* self, int64_t dim, bool keepdim, aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor)
{
    L2_DFX_PHASE_1(aclnnArgMin, DFX_IN(self, dim, keepdim), DFX_OUT(out));

    auto ret = CheckParams(self, dim, out);
    CHECK_RET(ret == ACLNN_SUCCESS, ret);

    auto uniqueExecutor = CREATE_EXECUTOR();
    CHECK_RET(uniqueExecutor.get() != nullptr, ACLNN_ERR_INNER_CREATE_EXECUTOR);

    int64_t dimNum = self->GetViewShape().GetDimNum();
    if (self->IsEmpty() || dimNum == 0) {
        if (dimNum == 0) {
            ret = HandleScalar(out, uniqueExecutor.get());
            CHECK_RET(ret == ACLNN_SUCCESS, ret);
        }
        *workspaceSize = 0;
        uniqueExecutor.ReleaseTo(executor);
        return ACLNN_SUCCESS;
    }

    auto selfContiguous = l0op::Contiguous(self, uniqueExecutor.get());
    CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);

    const aclTensor* argminOut = nullptr;
    // if self is 8 dims and will run tf kernel, it should merge axes
    if (CheckNeedReshape(self)) {
        FVector<int64_t> newShapeVector;
        FVector<int64_t> outShapeVector;
        auto realDim = CalculateShape(selfContiguous, newShapeVector, outShapeVector, dim, keepdim);

        aclIntArray* newShapeArray =
            uniqueExecutor.get()->AllocIntArray(newShapeVector.data(), MAX_SUPPORT_DIMS_NUMS - 1);
        CHECK_RET(newShapeArray != nullptr, ACLNN_ERR_INNER_NULLPTR);
        selfContiguous = l0op::Reshape(selfContiguous, newShapeArray, uniqueExecutor.get());
        CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);

        argminOut = l0op::ArgMin(selfContiguous, realDim, keepdim, out->GetDataType(), uniqueExecutor.get());
        CHECK_RET(argminOut != nullptr, ACLNN_ERR_INNER_NULLPTR);

        aclIntArray* outShapeArray = uniqueExecutor.get()->AllocIntArray(outShapeVector.data(), outShapeVector.size());
        CHECK_RET(outShapeArray != nullptr, ACLNN_ERR_INNER_NULLPTR);
        argminOut = l0op::Reshape(argminOut, outShapeArray, uniqueExecutor.get());
    } else {
        // if self.dtype is IntegralType, it cast self.dtype to int64 and it run aicore
        if (IsIntegralType(self->GetDataType(), true)) {
            selfContiguous = l0op::Cast(selfContiguous, op::DataType::DT_INT64, uniqueExecutor.get());
            CHECK_RET(selfContiguous != nullptr, ACLNN_ERR_INNER_NULLPTR);
        }
        argminOut = l0op::ArgMin(selfContiguous, dim, keepdim, out->GetDataType(), uniqueExecutor.get());
    }
    CHECK_RET(argminOut != nullptr, ACLNN_ERR_INNER_NULLPTR);
    CHECK_RET(CheckShapeAndScalarSame(argminOut, out), ACLNN_ERR_PARAM_INVALID);

    auto castResult = l0op::Cast(argminOut, out->GetDataType(), uniqueExecutor.get());
    CHECK_RET(castResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

    auto reformatResult = l0op::ReFormat(castResult, out->GetViewFormat());
    CHECK_RET(reformatResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

    auto viewCopyResult = l0op::ViewCopy(reformatResult, out, uniqueExecutor.get());
    CHECK_RET(viewCopyResult != nullptr, ACLNN_ERR_INNER_NULLPTR);

    *workspaceSize = uniqueExecutor->GetWorkspaceSize();
    uniqueExecutor.ReleaseTo(executor);

    return ACLNN_SUCCESS;
}

aclnnStatus aclnnArgMin(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, const aclrtStream stream)
{
    L2_DFX_PHASE_2(aclnnArgMin);
    return CommonOpExecutorRun(workspace, workspaceSize, executor, stream);
}

#ifdef __cplusplus
}
#endif
